Machine Learning

Machine learning investigates and develops methods that allow computers to infer or recognize patterns using datasets of various sizes, whether for exploratory purposes or to accomplish specific tasks. It has applications in numerous areas, from information systems and bioinformatics to computer vision, robotics, and security, among others.

Groups and Researchers in this Field

Perceiving Systems

Michael J. Black is one of the founding directors of the Max Planck Institute for Intelligent Systems, where he leads the Perceiving Systems Department. His research addresses a variety of topics relating to computer vision and perception: the statistics of natural scenes and their motion; articulated human motion pose estimation and tracking; the estimation of human body shape from images and video; the representation and detection of motion discontinuities; and the estimation of optical flow. His early work on optical flow has been widely used in Hollywood films. He also does research on neural engineering for brain-machine interfaces and neural prostheses. He is an honorary professor at the University of Tübingen, visiting professor at ETH Zürich, and adjunct professor (research) at Brown University. Read more

Michael J. Black

Machine Learning and Large-scale Data Mining Methods

Manuel Gomez Rodriguez is a research group leader at the Max Planck Institute for Software Systems. He is interested in developing machine learning and large-scale data mining methods for analysis and modeling of large real-world networks and processes that take place over them. His research comprises several dimensions: developing models of these networks and processes, assessing their theoretical properties and limitations; developing machine learning algorithms to fit the models and computational methods to influence processes over networks; and validating models and methods on gigabite- and terabyte-scale real-world datasets. Ultimately, he aims to provide computational tools with applications in a variety of domains, e.g. social and information sciences, economics, decision theory, causality, and epidemiology. Read more

Manuel Gomez Rodriguez

Computer Vision

Bernt Schiele is the founder of the Computer Vision and Multimodal Computing Department at the Max Planck Institute for Informatics, and head of its Computer Vision research area. His group focuses on multimodal sensor processing as well as computer vision. In computer vision, they consider problems of 3D understanding of images and video, such as object class recognition, people detection and tracking, and understanding traffic scenes. In multimodal computing, they are focusing on human activity recognition as a means to study how ubiquitous or wearable computing may benefit from better sensor understanding. Their research also involves machine learning, which is instrumental to inferring higher-level information from noisy sensor data and handling large-scale multimodal databases and sensor streams. Read more

Bernt Schiele

Empirical Inference

Bernhard Schölkopf directs the Empirical Inference Department at the Max Planck Institute for Intelligent Systems. The department investigates problems of empirical inference, i.e. inference based on empirical data. The type of inference can vary, including for instance inductive learning (estimation of models such as functional dependencies that generalize to novel data sampled from the same underlying distribution), or the inference of causal structures from statistical data (leading to models that provide insight into the underlying mechanisms, and make predictions about the effect of interventions). Empirical data may also vary, from sparse experimental measurements (e.g. microarray data) to visual patterns. The department uses theoretical, algorithmic, and experimental approaches to study these problems. Read more

Bernhard Schölkopf

Machine Teaching

Adish Singla is a research group leader at the Max Planck Institute for Software Systems. He is interested in the design of AI-ML methods that interact with, learn from, and teach other learning entities such as humans, robots, and machines. His research interests span various application domains, including the design of intelligent tutoring systems for personalized education, social robotics, and adversarial machine learning. The theoretical aspects of his work include machine learning (esp. online, active), AI (esp. probabilistic modeling), and optimization (esp. submodular). The focus is towards designing principled techniques that are both theoretically well-founded with strong provable guarantees and are practically applicable. Read more

Adish Singla

Exploratory Data Analysis

Jilles Vreeken is a senior researcher in the Databases and Information Systems Department at the Max Planck Institute for Informatics, and leads the Exploratory Data Analysis independent research group at the Cluster of Excellence on Multimodal Computing and Interaction. His research focuses on exploratory data mining: developing theory and algorithms to identify interesting structures within given data. Of particular value here are statistical methods, such as information-theoretic principles of minimum description length and maximum entropy. Next, he develops efficient algorithms to extract these structures from large and complex data, and investigates how they can be used in a range of applications, including identifying rare diseases, e-health, bio-informatics, market analysis, product recommendation, etc. Read more